I am new to data science and ML. Recently I have been given a sales dataset which contains weekly sales of a fashion brand. It has information about the product like category(t shirt, polo shirt, cotton shirts, briefs, jeans, etc.), gender (male, female, unisex), partner stores ( multiple retail outlets), the color of the apparel (some 150 color codes in hex format), MRP, Sold MRP and quantity sold. The data is for two years timeframe.
I added a new column called discount which is deriver from sold mrp/qty and mrp.
Can anyone suggest any kind of predictive modelling scenario for the above kind of data? I have already done time series forecasting and clustering based on high-performing partner stores. I tried Regression but how do you perform regression when most of the independent variables are categorical in nature ?
Any help would be much appreciated.